Feature engineering plays an important role in the success of a machine learning model. Most of the effort in training a model goes into data preparation and choosing the right features to represent the data. Often, features in the original data are not optimal and feature engineering is required to learn a good representation as an input to machine learning models. Recent advancements in domain specific feature engineering methods in areas of text mining, speech recognition, and emotion recognition have shown promising results. Analyzing high-dimensional datasets, however, can be challenging and computationally expensive. Moreover, such datasets often include features that are irrelevant to the task at hand.